{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DSNet:  Automatic Dermoscopic Skin Lesion Segmentation\n",
    "\n",
    "\n",
    "* Through  this  study,  we  present  a  new  and  automatic  semantic  segmentation  network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet).\n",
    "\n",
    "* In order to reduce the number of parameters to make the network lightweight, we used a depth-wise separable convolution in lieu of standard convolution to project the learnt discriminating features onto the pixel space at different stages of the encoder.\n",
    "\n",
    "#### This code is dedicated for the plotting of the ROC curves for proposed DSNet, UNet, and FCN8s. The class wise ROC curves are also been plotted using this code. \n",
    "\n",
    "* For any query: \n",
    "        ** Md. Kamrul Hasan \n",
    "        ** M.Sc. in Medical Imaging and Applications (MAIA)\n",
    "        ** Erasmus Scholar [2017-2019] \n",
    "        ** Contact: kamruleeekuet@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the python Packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import os\n",
    "import skimage.io as io\n",
    "import skimage.transform as trans\n",
    "import numpy as np\n",
    "import glob\n",
    "from PIL import Image\n",
    "import skimage\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from skimage.morphology import disk\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from skimage.measure import label, regionprops\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from matplotlib import pyplot\n",
    "from sklearn.metrics import roc_auc_score\n",
    "axisRange = [-0.01, 0.1, 0.2, 0.3, 0.4,0.5,0.6,0.7,0.8,0.9,1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data for plotting the ROC curve.\n",
    "#### Each .txt file contain the total images after flattening where “true_xx.txt” contains the true values of the masks [0,1] and “predict_xx.txt” contains the predicted probability from the network. The dimention of the contents in a .txt file is \"Image_Number x Rows x Columns\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For FCN on ISIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_fcn, tpr_fcn, thresholds_fcn = roc_curve(np.loadtxt('true_fcn.txt'),\n",
    "                                             np.loadtxt('predict_fcn.txt'))\n",
    "\n",
    "auc_fcn = roc_auc_score(np.loadtxt('true_fcn.txt'),\n",
    "                        np.loadtxt('predict_fcn.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For UNet on ISIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_unet, tpr_unet, thresholds_unet = roc_curve(np.loadtxt('true_unet.txt'),\n",
    "                                                np.loadtxt('predict_unet.txt'))\n",
    "\n",
    "auc_unet = roc_auc_score(np.loadtxt('true_unet.txt'),\n",
    "                     np.loadtxt('predict_unet.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For DSNet on Melanoma of ISIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_isic_mel, tpr_isic_mel, thresholds_isic_mel = roc_curve(np.loadtxt('true_isic_mel.txt'),\n",
    "                                                            np.loadtxt('predict_isic_mel.txt'))\n",
    "\n",
    "auc_isic_mel = roc_auc_score(np.loadtxt('true_isic_mel.txt'),\n",
    "                             np.loadtxt('predict_isic_mel.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For DSNet on SK of ISIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_isic_sk, tpr_isic_sk, thresholds_isic_sk = roc_curve(np.loadtxt('true_isic_sk.txt'),\n",
    "                                                         np.loadtxt('predict_isic_sk.txt'))\n",
    "\n",
    "auc_isic_sk = roc_auc_score(np.loadtxt('true_isic_sk.txt'),\n",
    "                            np.loadtxt('predict_isic_sk.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For DSNet on Ben/ Nev of ISIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_isic_ben, tpr_isic_ben, thresholds_isic_ben = roc_curve(np.loadtxt('true_isic_ben.txt'),\n",
    "                                                np.loadtxt('predict_isic_ben.txt'))\n",
    "\n",
    "auc_isic_ben = roc_auc_score(np.loadtxt('true_isic_ben.txt'),\n",
    "                         np.loadtxt('predict_isic_ben.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For DSNet on ISIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_isic, tpr_isic, thresholds_isic = roc_curve(np.loadtxt('true_isic.txt'),\n",
    "                                                np.loadtxt('predict_isic.txt'))\n",
    "\n",
    "auc_isic = roc_auc_score(np.loadtxt('true_isic.txt'),\n",
    "                         np.loadtxt('predict_isic.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For DSNet on PH2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_ph, tpr_ph, thresholds_ph = roc_curve(np.loadtxt('true_ph.txt'),\n",
    "                                          np.loadtxt('predict_ph.txt'))\n",
    "\n",
    "auc_ph = roc_auc_score(np.loadtxt('true_ph.txt'),\n",
    "                       np.loadtxt('predict_ph.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For DSNet on Melanoma of PH2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_ph_mel, tpr_ph_mel, thresholds_ph_mel = roc_curve(np.loadtxt('true_ph_mel.txt'), \n",
    "                                                      np.loadtxt('predict_ph_mel.txt'))\n",
    "\n",
    "auc_ph_mel = roc_auc_score(np.loadtxt('true_ph_mel.txt'),\n",
    "                           np.loadtxt('predict_ph_mel.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For DSNet on Ben/ Nev of PH2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr_ph_ben, tpr_ph_ben, thresholds_ph_ben = roc_curve(np.loadtxt('true_ph_ben.txt'),\n",
    "                                                      np.loadtxt('predict_ph_ben.txt'))\n",
    "\n",
    "auc_ph_ben = roc_auc_score(np.loadtxt('true_ph_ben.txt'),\n",
    "                           np.loadtxt('predict_ph_ben.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class wise ROC curves for the segmentated masks using the proposed DSNet for mel and nev classes of PH2 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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lrCEug+Eq49SpeK69dhbjxl3L009fg7e3+7ZDmkeeyxATE0O/fv1YvXo1o0eP5l//+leB3PfYMfj+e8uorFzZDVWrR/Lii3DHHdYDdAnonVvGYtMm6vzxB6SP9Q4fDmvXXuq+bdgAO3ZAQIA1lFW3rnUeEHDpvHLlS/d0rKS7iPOQjcFQwtm+/QQrVhzkscc6s2vXE1SuXMbtbRoDkw2nT5/mpptuujiZP3DgwHzdLyEBfv4Zvv3Wmsu0262H7eHDD/D88w1p2bLwJ7YLDVWr97FundUb2bzZOhxLqZuCNefh5wf33WeNBaazYIFHJBsMVxPJyWlMmbKCf/97A1Om3ARQKMYFjIHJltKlS9OqVSumTp1K796983yffftg+nSYOdOaY6lb15pDHjYMmjeHkJCDtGrVsACVFwHi4qy5iVKlYM4cGDfO6q2ANUcQFGStsurQAdq3Z8W5c3RPn6ju189zug2Gq5R3311FaGg0mzePJiCgYs4VChBjYDKhqlSoUIE5c+bk+R7bt8Orr1q9FpvNGsofM8ZarGS72tyLnnF456lc2ZpIuvlma06je3eoXRtuvNHqlXTtak0opa9ycpBWCBtfDYaSRnx8Cq+9FszQoa0ZN+46vL1tiAeGSa62n7t88c8//9C5c2f279+fp/oHD1orvdq0sVaETZxoXfvxR2sK4aowLqrW8tl33rGMiL+/tWcCrHG/iRMtwwKWcZkzB5580ppcymRcDAZDwRMcvJ/WracTHR1LQEBFSpXy8ohxAdODuUhaWhqPP/448fHx1EjffOUisbEwaZK1OlYEnn/eGgqrWtU9Wgud1FRYvtxamfC//1mbdMAa5pow4dKehWrVYPJkz+k0GEo4SUmpTJz4Nx99dCv9+jXztBxjYNKZP38+O3bs4Pvvv7+4adIVVq60Fj/t3w8PPWQZmrp13aez0Hn+eWt1wqlT1kbEvn2tuZI+fawdnQaDweP88ksEX38dxvz5A1m16mGP9VgyYwwM1rzLlClTCAwMZMCArM4UsyMtzRoNmjoVGjSwph1uuMGtMguH9est9yNvvWWlY2PhlltgwADLqHjYjbjBYLjEiRNxPP30H2zYcIQZM+4sMoYlHWNgsNyEh4WFMWPGDLwu51PFifh4uP9+axL/0Ufhgw+K8WZIVWvZcOPG1gbH9eutsb4nnoBatS7NrxgMhiKDqqIKa9YcJiCgAjNnPkbZskVvA93VMO2cb9q2bcvXX3/NoEGDcix74oS1GmzRIpg2zfIpWCyNy9Gj8O67dH74YWsu5ccfresPP2ztBq1Vy7P6DAZDthw+fI5+/eby3Xdb6N+/Oe++27tIGhcwBgaAihUrMmLEiItBuS5HRITlvmXbNli40FocVaxISIC5c62hrrp1Ydw4UsuWhc8+g7vussqULWvNtRgMhiKFqvL556F06PAlXbvWYciQXDr09AAlfohs3bp1rF27lkcfffSKk/u7dlmrckWs+ZZ0n4jFgtWrLb9c339vefutW9da/fXgg2w+erTQXYMbDIbckZCQgq+vN1FR5wkJGU7LltU9LcklSnwPZv78+ReDeV2OqCjLm7jNBqtWFRPj4hxa99VXrTgdd91luWo+cMCaxA8M9Jg8g8GQM6mpdt59dxUtW35GUlIab711Y7ExLuBhAyMifUQkQkQiRWR8Nvn1RWSZiGwVkRARCXDKGy4iexzH8LxqCA4OpmvXrpftvcTFWds8zpyBP/6wHFMWeT7/3NrsmB46+csvrfPZs63Nj1fFjk+D4epm9+7TdOs2kyVL9vLXXw/i61v8Bpw89ksjIl7Ap0BfrJi5Q0QkKFOx94BvVbUNMBmY4qhbBSt2TFegC/C6U/Axlzl37hybNm2iV69ely0zdqzl+uXHH3OOP+Ux9uyBl1665Hb+ppvgtdcuuWVu3LiYrkQwGEoeycl2YmISKFPGmzFjOrJ06TAaNcr1z1uRwJOPsl2ASFXdp6rJwDygf6YyQcAyx3mwU/6twFJVjVHVM8BSoE9uBaxfvx673U737t2zzZ8925q6ePXVSwH3igx2u7Wr/sYbraGu99+33NuD1c169dUSGJHMYCjerFlzmFGjNvLVVxupW9ePRx7pUOT2tuQGUecYvIXZsMi9QB9VHelIDwO6qupYpzL/Bdap6scicg8wH6iGFdnSV1XfcpR7FUhQ1feyaWcUMArA39+/4w8//HAx77fffuODDz5gwYIF+KV79HUQE1OaBx/sQuPGsXzwQdhlQw7nh9jY2BxXrmVGUlOp/tdf1Js3j3IHD5JQsybRt9/Osb59Sc6Db5q8aChIPN2+0WA0FBUNM2bs5/ffjzFyZB369KnrUcOS+T3o1avXRlXtlOsbWRt2Cv8A7gNmOKWHAZ9kKlMbWABsBj4GogA/4EXgFadyrwLP59RmYGCgZiYhISHLNVXVBx5QLV1aNSIi2+wCITg42PXCcXGq06ap1qunCqpt2qjOnauaklJ4GtyAp9s3GowGT2sIC4tWVdUlSyL11Km4IvkeAKGah995Tw6RRQHOXrsCgKPOBVT1qKreo6rtgYmOa+dcqesqvtl4+A0JuRTKpMgstPr0U3jqKWuJ8a+/Wh6NBw82cdgNhmLK2bOJPPLIz/TrN5eTJ+Po3bsxVau67gexOOBJA7MBaCoiDUWkNDAYWORcQESqiUi6xgnALMf5EqC3iFR2TO73dlxzGVXlrrvu4sf0HewO7HZ45hnLv9iECbl+TQVHUpLlg+aXX6z0o49aHo1XroTbb7+KQ2AaDFc/e/acpmXLz/D19Wb79sfx9786ffx5zMCoaiowFssw7AR+UNVwEZksInc6ivUEIkRkN1ADeNtRNwZ4E8tIbQAmO665zJkzZ/j555+JiorKcP3nn2HLFsvrfC6cKhc8NpsVCvP33610pUrWTk+DwVBsOX48lvXrj9CwYWUWLhzEp5/eTsWKPp6W5TY8uiFCVReraqCqNlbVdOPxmqoucpz/pKpNHWVGqmqSU91ZqtrEcXyd27YPHz4MQF0n3/qq8Oab1iKsIUPy++ryQHi4Nex1/ry1xHjNGsuNi8FgKNaoKt9+u4U2bT5n+fKDeHvb6NKljqdluZ0SO4Cf3nMJCLi4d5NVqyzHwl9+WchTGwkJ1r6VDz+0PBpv2wbXXWedGwyGYs8LL/zJsmX7Wbz4fjp2rO1pOYVGid3SnV0PZvp08POzXPEXGitWQNu28N57lifjPXss42IwGIo1drsyc+YmLlxI4rnnurFhw6MlyrhACe7B2Gw2GjZseDE88vnzVkTgkSMLKaZWXBxNpk2zNkvWr2/5CLvxxkJo2GAwuJuIiFOMHPkLaWl2evduTN26fjlXugopsT2YUaNGsW/fPrwdY2G//GIt3CqUuZe//4bWrQlYuNDyRbNtmzEuBsNVwqlT8fTo8Q0DBwaxYsVDJda4QAnuwWTmxx+hTh3o1s3NDe3aBTffDI0bs/njj2n/1FNubtBgMBQGYWHHWLHiIE8+2ZXdu5+8qleHuUqJ7cGMGDGC8eMtB84XLlieku+9142Ohvfutf42b27FZdmyhXNt2ripMYPBUFgkJqYyceIyevf+7qJRMcbFosQamLVr17J//34AgoOt4bH+mV1tFhTffAPNmllL1ADuu8/Dm2wMBkNB8d57q9m58xRbtoxh+PB2npZTpCixQ2Rnz56lUqVKAPz1lxUl+NprC7iR2FjLTf5dd1lRy1q0KOAGDAaDJ4iNTWbixGU8+GBbxo+/Hm/vEvusfkVK7LuS2cDccAP4FFSvNinJmry//npITrZ24b/yCmTj98xgMBQvliyJpFWrzzh3LomGDSsb43IFSmQPJjExkaSkJCpVqsTRo7Bzp7UFpUDYuxcGDoRNm+C55wropgaDoSiQlJTK22+v4Isv7uDWW5t4Wk6Rp8QamOuuu45GjRqxbp117frrC+DGf/8Nd99trRT43//cOKljMBgKk/nzdzB79hZ+/nkwy5c/5Gk5xYYSaWAqVarEypUrAZg40XIL07ZtPm+6fDnccQc0amS502/QIN86DQaDZ4mOvsDYsb8THn6CmTPvLNbRJT2BRwcPRaSPiESISKSIjM8mv56IBIvIZhHZKiK3OeVNcNSLEJE8BzQODYVWraxJ/jyzZo3lQj99R74xLgZDsUZVsduV0NCjNG9elbCwMVx3XT1Pyyp2eMzAiIgX8CnQFwgChohIUKZir2C58W+PFS/mM0fdIEe6JdAH+MxxP5cIDw+nZcuWBAeHEBoKnXIfCPQSGzZAnz5Qs6ZlXByuZwwGQ/Hk2LFEbr11Dt99t4V+/Zrx9ts34etbIgd78o0nezBdgEhV3aeqycA8IPOkhQLpLoX9uBS1sj8wT1WTVHU/EOm4n0ucP3+eHTt2cOhQIjEx0LFjPl7FrFlQtao1/1K7ZDmyMxiuJlSVTz5Zx+jRG7nxxobcf39rT0sq9njSLNcBDjulo4CumcpMAv4UkSeBcsDNTnXXZqqbbXAFERkFjALw9/cnJCSEsLAwANassVz2JyZuJiTkXN5exb33UvqWW0jeu/fSbn0XiY2NJSQkJG/tFhCe1uDp9o0GowEgISGNMmW82LTpAP/6VzOaN09l1aoVha4DrrLPQVU9cgD3ATOc0sOATzKVeQ543nHeDdiB1ev6FHjAqdxMYEBObQYGBqqq6pIlSxTQ555bpaB69KjmjgsXVIcOVT14MJcVMxIcHJyv+gWBpzV4un2joWRrSE5O1bfe+kcbNPhIExJSPKIhM55uPzsNQKjm4Xfek0NkUUBdp3QAl4bA0nkE+AFAVdcAvkA1F+telqQkKzDm0aM+lC9vTZ/kil27rFDGO3fmsqLBYCgq7Nx5ks6dv2LlysOEhAw38yxuwJMGZgPQVEQaikhprEn7RZnKHAJuAhCRFlgG5qSj3GAR8RGRhkBTYL2rDVetWpXevXtz9GhlAgMh1ysPO3WC/fvh1jwvXjMYDB4iISGFU6fiqVDBh+ef78bixfdTv34lT8u6KvGYgVHVVGAssATYibVaLFxEJovInY5izwOPisgWYC4wwtFjC8fq2ewA/gCeUNU0V9u+9tprWbJkCdHRjWjcOBeilyyxIk/a7SacscFQDFm+/CBt237ON9+EERBQkWHD2pq9LW7Eo31CVV0MLM507TWn8x1AtvGDVfVt4O38tB8dbW1fcYmzZ+Ghh6BKFXjiiXxunDEYDIXNSy8tZc6cbfz73325+27jeLYwyLEHIyKlReQuEXlfROaKyCwReU5EmheGQHfw7bffUq9efWJjT1KrlouVXngBTpyA2bONcTEYihEbN1rTs717N2b79seMcSlErmhgROQVYB3QC9gCzMaa//AGPhSRP0SkldtVFjBnzpzh8OFDgJdrE/zLlsHMmZaRydemGYPBUFicPh3Pgw8u5L77fuTkyThuuqkRlSubh8PCJKchsm2q+tZl8qaKSC0yruYqFqSvIgOfnA1MUhI8/jg0aQKvv+5uaQaDoQCIiDhFz56zGTy4Jdu2PUa5cqU9LalEckUDo6o/Xy5PRAJUNQqILnBVbsbZwOQ4RPbhh7B7t7Us2QyNGQxFmqNHL3Dw4Fm6dKnDb7/dT4cOro6BG9yBK3MwnR1zMNUc6ZYi8i0Zd9IXK5KTkx0rR3IYIjt+HN56y4pI2adPYckzGAy5RFWZOXMT7dp9zrp1R/DyshnjUgS4Yg9GRKYAA7DmX14RkYXA08C/gDHul+cemjdvTosWg9i9W6ha9QoF33sPEhLgX/8qNG0GgyH3PPvsElauPMTSpcNo2za3O6cN7iKnOZj+QFtVTRCRKli75duqaoT7pbmPoUOHsmzZUM6ds2KDZYsqhIfD/fdDYGCh6jMYDDmTlmbnq682MXRoa1566Tree6+3CV9cxMjJwCSqagKAqsaIyK7iblzSOXUKqlW7QgERWLwYEhMLTZPBYHCN8PATPPLIInx8vOnXL5A6dczG56JITua+kYgscBwLgQZO6QWFIdAdTJo0iSVLauHnd5kCiYmWBQLw9S00XQaDIWdOnozj5pu/Y8SIdgQHDzfGpQiTUw9mQKb0v90lpDBJSEggJeXs5b29/PgjPPIIbN4MLVsWqjaDwZA9G0d8xtkAACAASURBVDYcYeXKQzz7bDf27HmS8uXN0uOizhV7MKq6DDgBVAAOqeoy56NQFLoBy/v0FdyJde0KL78MQZkDbBoMhsImPj6FF1/8kzvumEuNGuUBjHEpJuS0iuxlLJf5m4DOIjJZVWcVijI3oyqXNzCBgTBpUmHKMRgMl+GDD9YQFXWBbdseo3r1cp6WY8gFOc3BDAXaqOp9QGfgsYJsXET6iEiEiESKyPhs8j8UkTDHsVtEzjrlDReRPY5jeF7az3YOZuFCyzWMwWDwGOfOJfLYY78SGnqUCROuZ+7cAca4FENyMjBJqhoHoKonXSjvMiLihRWZsi8QBAwRkQxjUqr6rKq2U9V2wCfAAkfdKsDrWCGWuwCvi0hlV9tu27YTMCxrD0bV8jc2dWpeX5bBYMgnv/22m1atppOWpjRtWgUvL7P0uLiS0yR/I6fVYgI0dl49pqr35KPtLkCkqu4DEJF5WPtudlym/BAsowJwK7BUVWMcdZcCfbBixuRI794DgYFZDUxoKOzbB6+8kqsXYjAYCobkZDsffLCW2bPv4sYbG3pajiGfSPqEd7aZIjddqXJ+JvpF5F6gj6qOdKSHAV1VdWw2ZetjuaYJUNU0EXkB8E13xCkirwIJqvpeNnVHAaMA/P39O/7www8cO+bDkCHdePHFXdx227GLZRtPn06dBQtYvXAhqeXL5/WluUxsbCzlC6GdoqzB0+0bDZ7XoKoEB5/kzz+PM3FiAypUqFDoGpzx9Gfh6faz09CrV6+Nqtop1zdS1csewMwr5efnAO4DZjilhwGfXKbsS855wIvAK07pV4Hnc2ozMDBQVVUfeeRFhYo6Z45ewm5XrVdPtV8/LSyCg4MLra2iqsHT7RsNntUQFXVO+/X7r7Zs+amuWxdVYt+HotR+dhqAUM3D73xOg5vtc22xXCeKjK7+A7Bc0WTHYDIOf+WmbhZSU+1AGqWdVzru3g2HDsEdd7h6G4PBkEdUlbQ0O2Fhx+jQoRabNo2mS5c6npZlKGBymoMpKyKtseZfsqCqW/PR9gagqYg0BI5gGZH7MxcSkWZAZWCN0+UlwDtOE/u9gQmuNpyaag0L+vg4XfzrL+vvTVccFTQYDPkkMjKGRx/9hREj2jJ8eDtuv934+rtaycnA1MFa6ZWdgVHghrw2rKqpIjIWy1h4AbNUNVxEJmN1xxY5ig4B5jm6ael1Y0TkTSwjBTBZHRP+rpCWBiAZezDLlkH9+tCoUV5fksFguAKqygcfrGHKlJW8/HJ3HnigjaclGdxMTgYmUlXzbERyQlUXA4szXXstU3rSZerOAvK06dMyME49mLQ0CA6GAQMsJ5cGg6FAOX8+iYoVfUhMTGXt2pE0aVLF05IMhUBOBuaqJCioO2C7ZGCOHoVateDmmz0py2C46khKSuWdd1bw7bdb2bXrCSZOdNvzqqEIkpOBeblQVBQyHTveBdx1aYisbl3YcbntNwaDIS+Eh59g0KCfaNSoMitXPoSPT4l8ni3R5PSJj7ZCC7NUVVOdMxx7U4YDUVrM/JPFxaUAafj4GFf8BkNBExeXTHx8CpUrl+GVV25g0KCWjhDlhpJGTsuUnwBuAXaLyBoRWSQif4rIHuBrILy4GReA2bPHATUu9WC6dIG33/akJIPhquDvv/fTps3nzJmzldq1KzB4cCtjXEowV+zBqOoR4DngORFpAtQCEoAIVb1QCPrcQoZJ/tRUaNPGGiYzGAx55rnnlvDTTzuYPv12s/TYAORikl9VI4FIN2opNNLSrBXPpUsD3t4wY4ZnBRkMxZi1a6Po2rUO/fs3Y9KknlSs6JNzJUOJoES6KU3fB+PjA8THW16UDQZDrjhxIo7Bg39i2LCFnD6dQI8eDYxxMWSgBBsYxxDZc89BgwaelGMwFDt27TpF69bTqVfPjy1bxlCtWllPSzIUQVweIhOR0kA9x1BZsaZhw1tYubKqNUS2cycEBHhaksFQLDh8+BwHD56jW7cA/vprGK1b1/C0JEMRxqUejIjcDmwDljrS7URkoTuFuZOGDW8HXqVUKSwD06KFpyUZDEUau12ZPn0DHTp8yebN0Xh52YxxMeSIqz2YyVjRI4MBVDXMsaqsWJKQEIvNloicBk6ehKCgHOsYDCWZp5/+ndDQaP75ZwRBQf6elmMoJrg6B5OiqmczXSu2M+PBwS9jtwfCrl3WBdODMRiykJpqZ9q0dZw7l8grr9zAypUPGeNiyBWuGpidIjIQsIlIQxH5CCvCZL4QkT4iEiEikSIy/jJlBorIDhEJF5H/Ol0fLiJ7HMfw3LR70THzgQPWX+NB2WDIwJYtx7jmmhksWhRBXFwKNWqUx8urRK4JMuQDV78xY4GOgB1YACQCT+enYRHxwgoF0BcIAoaISFCmMk2x4rxcp6otgWcc16sAr2MN23UBXneKDeOqAjh82Do1mywNhoucPBlH377/4fHHO7N06TBq1/ZsCGND8cXVOZhbVfUlrNDFAIjIPVjGJq90wQoHsM9xv3lAf8DZ6+SjwKeqegZAVU+k68HyjxbjqLsU6EPGqJeX5WIP5vBhqFIFypollgZDePg5QkNX88IL1xIZ+RRly5bytCRDMcfVHswr2VybmM+26wCHndJRjmvOBAKBIrJKRNaKSJ9c1M0Bgagos0TZUOKJjU3mmWf+4LXXdlC/vh+AMS6GAuGKPRgRuRWrZ1BHRD5wyqqINVyWHy4XJdMZb6Ap0BMIAFaISCsX61qNiIwCRgH4+/sTEhKCn19XbLYm7Ao6hq1xY46GhOTxJeSP2NhYQjzUdlHR4On2jQb47ruDREUl8MknLfD3P2m+D+Z/ouA0qOplD6A98Ahw0PE3/RgIVLtS3ZwOoBuwxCk9AZiQqcznwAin9DKgM1YY5S+crn8BDMmpzcDAQFVVfekl1dKl1eMEBwd7WoLHNXi6/ZKqISYmXh955Gddty5K09LsHtGQHUaD59vPTgNWGPtc/85fcYhMVTer6kygmarOdDp+UNVT+bRtG4CmjlVppYHBwKJMZf4H9AIQkWpYQ2b7gCVAbxGp7Jjc7+245hLx8adAD8ChQ5CcnM+XYTAULxYu3EmrVtPx9fWmRYtq2GzGnb7BPbg6yV9HRN7GWu11MUqXqubZJ7eqporIWCzD4AXMUtVwEZmMZS0XccmQ7ADSgBdV9TSAiLyJZaQAJqtjwt8VVq58ndSU76H+afjoI3g6XwviDIZigaqSlJTG559vZN68AXTvXt/TkgxXOa4amG+At4D3sJYVP0T+52BQ1cXA4kzXXnM6VxzxaLKpOwvIU7AzVbUmbL76Crp1y8stDIZig6ry3Xdb+c9/tvHHH0NZsuQBT0sylBBcNTBlVXWJiLynqnuBV0RkhTuFuRvFBiNHelqGweBWDh48y+jRv3L8eBwzZ95poksaChVXlyknifXN3CsiY0SkH1DdjbrcijX5ZIfQUDMHY7gqsduVtDQ7O3ac5IYb6rN+/Ug6dKjlaVmGEoarBuZZoDzwFHAd1gbIh90lqjDwIRk6d4bTpz0txWAoUCIiTnHDDV/zn/9so2/fprz8cndKlfLytCxDCcQlA6Oq61T1gqoeUtVhqnon1tLlYkmzZoO40dbbSvj5eVaMwVBAqCpTpqzguutmMWhQSx54oI2nJRlKODnOwYhIZ6xd8itV9ZSItMRyGXMj1ubHYke9ej3xty0F8YIyZTwtx2DIN2fPJlKpki9eXjZCQ0fRoEElT0syGK7cgxGRKcB/gKHAHyIyESsmzBasPSnFkvPno4jVfVbvxUx6GooxiYmpvPzyMtq3/4KkpFTGjbvOGBdDkSGnHkx/oK2qJjg8GB91pCPcL819rF37JofS/sfkijU9LcVgyDPbth3nvvt+pFWr6qxZ8wg+Pi5HQDcYCoWcvpGJqpoAoKoxIrKruBsXAFUQ1My/GIolFy4kER+fQrVqZXnnnZu45x4TMM9QNMlpkr+RiCxwHAuBBk7p/Ljq9zBqDIyhWLJkSSStW0/n++/DqVWrgjEuhiJNTj2YAZnS/3aXkMJEFbywQ8WKnpZiMLjMU0/9zqJFEXz5ZT96927saTkGQ45c0cCo6rLCElLYmB6MobiwYsVBrr++HgMHtuSdd26ifPnSnpZkMLhEiZwVDAp6iCoR5WHMPZ6WYjBclujoC4wd+zs7dpxk5cqHuP76ep6WZDDkCld38l9V1K7djbAyH8D113taisGQLTt3nqRt289p0aIamzePpmpVE9bbUPzIlYEREZ+CbFxE+ohIhIhEisj4bPJHiMhJEQlzHCOd8oaLyB7HMTw37cbE7KF50ldw+HDOhQ2GQuTAgbMsX36QZs2q8c8/I3jrrRvx9S2RAw2GqwCXDIyIdBGRbcAeR7qtiHySn4ZFxAv4FMv9fxAwRESCsin6vaq2cxwzHHWrAK8DXYEuwOuOwGMusTH0/ziSNMpy128wFAHS0pRp09bRqdOXhIefwGYTWrTw97QsgyFfuPpoNA24AyvCJKq6RUR65bPtLkCkqu4DEJF5WBs7d7hQ91ZgaXqQMRFZCvQB5rrSsAKnpRoMG5YX3QZDgTNtWiQxMd6sWvUwzZpV87Qcg6FAcNXA2FT1YKZYEmn5bLsO4DxGFYXVI8nMABG5AdgNPKuqhy9Tt052jYjIKGAUgL+/PyEhIcTFxZGEDyFHjsCRI/l8GXknNjaWkJAQj7VfFDR4un1PakhNtbNgwRFuu60WAwf6U6uWH9HR24mOLnQpQMn+LIqSBk+3X6AaVDXHA5iP1ePYhBXe+BngR1fqXuGe9wEznNLDgE8ylakK+DjOxwB/O85fBF5xKvcq8HxObQYGBqqqasvmD2pZqaq6b596kuDgYI+2XxQ0eLp9T2kIDT2ibdpM1z595mh09IUS+z4YDUWv/ew0YIWxz/XvvKuT/I9hhS2uBxwHrnFcyw9RQF2ndACWr7OLqOppVU1yJL8COrpa90qUSkuiip6GZVftNh9DEebEiTj695/HCy90Y/Hi+6lZs7ynJRkMbsHVIbJUVR1cwG1vAJqKSEPgCDAYuN+5gIjUUtX0AYM7gZ2O8yXAO04T+72BCa423KXJEO7a8z34+uZHv8GQK5YvP8iqVYeYMKE7kZFPmdVhhqseV3swG0RksWNpcIWCaFhVU4GxWMZiJ/CDqoaLyGQRudNR7CkRCReRLVjRNEc46sYAb2IZqQ3AZMc1l6hfqTF9AXwKdNW1wZAt588n8cQTv3H//fMvrgwzxsVQEnDpW66qjUXkWqxexhsiEgbMU9V5+WlcVRcDizNde83pfAKX6Zmo6ixgVl7aPXk6nBCgpzEwhkJg2rR1JCWlsX3741SqZHrNhpKDyxstVXW1qj4FdADOYwUiK5as3PUtw8EMkRncxqlT8Tz44ELWrYti4sTuzJhxpzEuhhKHqxsty4vIUBH5BVgPnASudasyN2JTxwpr04MxFDCqyg8/hNO69XSqVi1Dq1bVERM11VBCcXUgeDvwCzBVVVe4UU+hIOkGxvRgDAWIqpKUlMa3325h4cJBXHNNgKclGQwexVUD00hV7W5VUoiIPQ0B04MxFAiqysyZm5k3bztLlw7j11/vz7mSwVACuKKBEZH3VfV5YL6IaOZ8VS2W/u7NEJmhoNi37wyPPvoL584lMmtWfzMcZjA4kVMP5nvH36sikmU6zTpPpvSyTVDPxNcw5I20NDuqsGfPafr2bcIzz1yDt3eJjH5hMFyWnCJarnectlDVDEZGRMYCxXIrvE/tHuwo1wPKeVqJoTgSHn6CRx5ZxOOPd+bBB9ty661NPC3JYCiSuPrI9XA21x4pSCGFSeLeBbS/MAESEjwtxVCMUFUmT/6Hnj1n89BD7XjggTaelmQwFGlymoMZhLW5sqGILHDKqgCcdacwdxK5ZSpHEtbBuaehTBlPyzEUA06fjqdq1bKUL1+azZtHExBQ0dOSDIYiT05zMOuB01jOJD91un4B2OwuUe7mdPn6RJ86AlWrelqKoYgTH5/Ca68Fs2DBTnbufILnnuvmaUkGQ7EhpzmY/cB+4K/CkVM4pNhKkyKloFQpT0sxFGHCwo5x770/0KVLHdatG4mPj/EfZjDkhpyGyP5R1R4icgYrEOTFLEBVtYpb1bmJsklnqGA/b83BmCEyQybOn08iLi6ZmjXL8+GHt9KvXzNPSzIYiiU5TfKnh0WuBvg7HenpfCEifUQkQkQiRWT8FcrdKyIqIp2crk1w1IsQkVtz027FhONWPJi4uPzIN1yF/Prrblq2/IwFC3ZSs2Z5Y1wMhnyQ0xBZ+u79usBRVU0WkeuBNsAcLKeXeUJEvLDmdW7BCiC2QUQWqeqOTOUqYLnqX+d0LQhr8UFLoDbwl4gEqqpLYZzvCehDz1OhYDbFGZx47LFfWbp0H99+exe9ejX0tByDodjj6jLl/wEqIo2Bb4EWwH/z2XYXIFJV96lqMjAP6J9NuTeBqUCi07X+WOECkhzzRJGO+7lELd9qtAdjYAyoKps3n0FVGT68HVu3PmaMi8FQQLg6a2lX1RQRuQf4SFWniUh+V5HVAQ47paOArs4FRKQ9UFdVfxWRFzLVXZupbp3sGhGRUcAoAH9/f0JCQgg9ugVvoOaqVaRWKJD4aXkiNjaWkJAQj7VfFDR4sv2TJ5P48MPdHD0aT6NGf+HnV4r16yM9osXTn4PRUHQ0eLr9AtWgqjkeWMuV7wO2Yjm+BNjuSt0r3PM+YIZTehjwiVPaBoQADRzpEKCT4/xT4AGnsjOBATm1GRgYqKqq11XtoE1A9cwZ9STBwcEebb8oaPBU++HhJ7Ratak6aVKw/vnnMo9ocMbTn4PRUHQ0eLr97DQAoZqH33lXezAPA49juevfJyINgbn5smxWr6OuUzoAOOqUrgC0AkIcDgRrAosc4ZRzqpsDjgVxZoisxBEZGcPhw+fo0aMBq1Y9TGBgVY8/LRoMVysuzcGo6nasifZQEWkOHFbVt/PZ9gagqYg0FJHSWJP2i5zaPKeq1VS1gao2wBoSu1NVQx3lBouIj8PYNcXqZbmEpi+4NgamxJCaauf991dzzTUz2Lv3DDabEBhoNtoaDO7EpR6MiHQHvgOOYO2BqSkiw1R1VV4bVtVUh8PMJYAXMEtVw0VkMlZ3bNEV6oaLyA/ADiAVeEJdXEGWjjEtJYuxYxeze/dp1q0bSePGxXL7lsFQ7HB1iOxD4DZ1LCEWkRZYBqfTFWvlgKouBhZnuvbaZcr2zJR+G8hjL8oMkZUEkpJS+eijtYwe3Yl33rmJypV9TbwWg6EQcXWZcml12p+iqjuB0u6R5H4Cr59NtWq/QdmynpZicBPr1kXRseOXrF4dRXJyGlWqlDHGxWAoZFztwWwSkS+wei0AQynGzi6p0oYjZdtYA3OGq44TJ+IYOPAnpk69mYEDWxrDYjB4CFcNzBisSf5xWNMXy4FP3CXK3SSHf0yHU+shdTZ4GweGVwt//72f1asP88orN7Bnz5OULm2eIAwGT5Ljr6uItAYaAwtVdar7JbmfA5GzOB2/FVJnGgNzFXD2bCIvvvgnS5bsZfr02wGMcTEYigA5eVN+GSty5Sags4hMVtVZhaLMjURXDOTohQTw8fG0FEMB8Omn6ylVyovt2x+nYkXzmRoMRYWcHt+HAm1UNU5E/LFWfBV7A2PHRho2s4qsGHPiRBxPP/0HTz/dlZdf7m7mWQyGIkhOq8iSVDUOQFVPulC+WFAx8TjV7CchLVdbZwxFAFVlzpyttG49nXr1KtK2bQ1jXAyGIkpOPZhGIrLAcS5AY6c0qnqP25S5kQqJp0mxxzht6TcUB1SVpKQ05s/fyW+/3U+nTrU9LclgMFyBnAzMgEzpf7tLSGEyss5t3HpuuxkiKybY7coXX4Ty4487WLbsQRYuHORpSQaDwQVyCji2rLCEFCZ+3mWpDcbAFAN27z7NyJGLSEmxM3PmnWY4zGAoRpTINbprzoZzEhhtfqyKLKmpVjDVQ4fOMWBAC8aO7YKX11UxBWgwlBhK5H/sqrPbmQamB1NE2bLlGF27zmDevO3cfHMjnn76GmNcDIZiSK7+a0WkQDcZiEgfEYkQkUgRGZ9N/hgR2SYiYSKyUkSCnPImOOpFiMitBanL4BlUlVdf/ZtbbvmOsWM7M3Roa09LMhgM+cAlAyMiXURkG7DHkW4rIvlyFSMiXliRKfsCQcAQZwPi4L+q2lpV2wFTgQ8cdYOw4se0BPoAnznu5xKKWT1W1Dh5Mg4RoXr1cmzZMoaHHmpv5lsMhmKOqz2YacAdwGkAVd0C9Mpn212ASFXdp6rJwDygv3MBVT3vlCzHRT/79AfmqWqSqu4HIh33cxnz01U0SEhI45ln/qBbt5kkJ6fx5JNdqVWrgqdlGQyGAsBVA2NT1YOZruV3l2Id4LBTOspxLQMi8oSI7MXqwTyVm7qGos2mTdE8/HAoZ84ksm7dSOM/zGC4ynB1FdlhEekCqGMo6klgdz7bzq4TkWXsSlU/BT4VkfuBV4DhrtYFEJFRwCgAf39/QkJCKN/mJ85FisdjscfGxpZIDRcupJCUZMdmE8aMCaBHj0ps2+ZyxOsCp6R+DkZD0dTg6fYLVIOq5ngA1bGGsE45jnlANVfqXuGe3YAlTukJwIQrlLcB57IrixV2uVtObQYGBqqq6uDBqo5TjxIcHOxpCYWuYcGCHVq79vv62WfrPdJ+dhgNRkNR0uDp9rPTgBXGPte/8y71YFT1BNakekGyAWgqIg2BI4773+9cQESaquoeR/J2HIsMgEXAf0XkA6A20BRw+RE4bfMLtInaAizN3ysw5IpRo37hn38OMm/eALp3r+9pOQaDwc24ZGBE5CuyH74aldeGVTVVRMZi9T68gFmqGi4ik7Gs5SJgrIjcDKQAZ7CGx3CU+wHYAaQCT6iqy3NCh47+Smz8npwLGvKNqrJ06T5uuaURjz7agY8/7kOZMqU8LctgMBQCrs7B/OV07gvcTcZJ9jyhqouxQgA4X3vN6fzpK9R9G3g7L+1GVQziZFLpvFQ15IKDB88yevSvHD8eR6dOD9K5s1mHYTCUJFwdIvveOS0i31Gsx5fUbOJ3M+HhJ+jZczbPPXcNL7xwLaVKmRViBkNJI6++yBoCxXYQvUp8FN4pRzwt46okIuIUUVHn6dWrIevWjaRRo8qelmQwGDyEqzv5z4hIjOM4i9V7edm90txHueQzVLCf9bSMq4qUlDT+7/9Wcv31X3Po0DlsNjHGxWAo4eTYgxHLX0dbrJVeAHbHsrViy/had9Bz7wxPy7iqeOKJxRw6dI7Q0EepX7+Sp+UYDIYiQI4GRlVVRBaqasfCEFQYeCF4ifHOm18SE1N5773VPPFEZ6ZOvQU/Px/jP8xgMFzE1V/Z9SLSwa1KCpHfz23lPU32tIxizapVh2jX7nO2bDlOaqqdSpV8jXExGAwZuGIPRkS8VTUVuB541OETLA7LVYuqarE0Ohvj9hOvKUzytJBiyvHjsTzwwELef78399zTwtNyDAZDESWnIbL1QAfgrkLQUsiYp+3csmRJJKtXH+aNN3qxe/dYs/TYYDBckZwMjACo6t5C0FJomHgwuSMmJoHnnlvCP/8c5Isv7gAoEcYlJSWFqKgoEhMT3d6Wn58fO3fudHs7RkPR1+DJ9n19fQkICCiw++VkYPxF5LnLZarqBwWmpJAx/RfX+fzzUPz8fNi27THKly85HhCioqKoUKECDRo0cPv80oULF6hQwbNxcIyGoqHBU+2rKqdPnyYqKqrA7pmTgfECynOV/R57iTfeuYsWXeKIjr7Ak0/+zvPPd2PChOtL5AR+YmJioRgXg6EoICJUrVqVkydPFtg9czIw0ao6ucBaKyLU7L2HyEhPqyiaqCrffBPGSy/9xahRHWnfvlaJ/oEtya/dUPIo6O+7S3MwVxvFe5uo+7DbleTkNH7/PZI//xxGu3Y1PS3JYDAUY3IaJ7rJnY2LSB8RiRCRSBEZn03+cyKyQ0S2isgyEanvlDdcRPY4juG5anjDEJpEdimAV3B1kJZmZ9q0ddx442x8fLz44Yf7jHEpIkRFRdG/f3+aNm1K48aNefrpp0lOdv8ervLlywNw4MABWrVqlW2Z6Oho7rjDWvQREhKCn58f7du3p0WLFrzxxhsXr6eXSWfEiBH89NNPAAwdOpRmzZrRqlUrHn74YVJSUgD49ddfef31113W+/TTT1OnTh3sdvvFa5MmTeK9997LUK5BgwacOnUKgGPHjjF48GAaN25MUFAQt912G7t35y9Qb1JSEoMGDaJJkyZ07dqVAwcOZFvu448/plWrVrRs2ZKPPvro4vWwsDBuvPFG2rVrR6dOnVi/3gpz9e6779KuXTvatWtHq1at8PLyIiYmJl9aC4MrGhhVddsrcIRe/hToCwQBQ0QkKFOxzUAnVW0D/ARMddStArwOdAW6AK+LiMuOrw6dDSUiMTz/L+Iq4NCheLp3/5qfftrBl1/2M0NCRQhV5Z577uGuu+5iz5497N69m9jYWCZOnJjve6empub7Hh988AGPPvroxXT37t3ZvHkzoaGhzJkzh40bN+Z4j6FDh7Jr1y62bdtGQkICs2fPBuD2229n0aJFxMfH53gPu93OwoULqVu3LsuXL3dJu6py991307NnT/bu3cuOHTt45513OH78uEv1L8fMmTOpXLkykZGRPPvss7z00ktZymzfvp2vvvqK9evXs2XLFn799Vf27LHiU40bN47x48cTFhbG5MmTGTduHAAvvvgiYWFhhIWFMWXKFHr06EGVKlXypbUwyKs35YKgCxCpqvsARGQe0B8riBgAqhrsVH4t8IDj/FZgaboBFJGlQB9grisNHynfjPN2v3y/gOJMSooVn+3UqSSGDWvD6NGdsNmMcbkczzwDYWEFe8927cDp4TULf//9N76+vjz00EMAeHl58eGHH9KwYUPeHKLoogAAIABJREFUeOMNevXqxaxZs2jZsiUAPXv25P3336d58+Y8+eSTbNu2jdTUVCZNmkT//v355ptv+O2330hMTCQuLo5FixbRv39/zpw5Q0pKChMnTmTwYNcD186fP5+33nory/Vy5crRsWNH9u7dS/Xq1a94j9tuu+3ieZcuXTh69ChgzQX07NmTX3/9lYEDB17xHsHBwbRq1YpBgwYxd+5cevbsmaP24OBgSpUqxZgxYy5ea9euHWCt4sorP//8M5MmTQLg3nvvZezYsahqhge3nTt3cs0111C2bFkAevTowcKFCxk3bhwicrH9c+fOUbt27SxtzJ07lyFDhuRZY2HiSQNTh4xBy6KweiSX4xHg9yvUzTaalYiMAkYB+Pv7ExISQlJSEna7nZCQkDxKLxhiY2M9oiEi4gJTp0YweHBdunUrR/nycSxf/k+h6wDPvQeuaPDz87v4z56c7ENaWsGuPExO/v/2zjyuinL/4+8voqCJikteiy7mRojgEpZLGriQ2XVJLTIrzaUsbbkuv1LTut682mp1s8XMK5oBWaK0aJFKmrngiluKGglqLuQCEvvz+2OG8Rw4wgEOHJB5v14HZp55lu+ZmTPfeZ5n5vPNIzU1E4Dc3NxCF7adO3fi7+9vlS4ieHl5sXfvXgYPHsyyZcuYMWMGf/zxB8nJybRp04aXX36Zrl278u6773Lx4kWCg4O58847ycjI4JdffuGXX36hYcOGZGdns3TpUurVq0dKSgq9evWif//+xsUwNTWVtLQ08vLyCtmWmJhI/fr1ycrKIisri/T0dHJyckhNTSUlJYUtW7YwadIkzp8/z6ZNmwgICDDKJiUl0bt3b6s6s7OzCQsL4z//+Y+R7ufnx7p167j33nuL3I9Lly5l8ODB9OnTh2nTpjF37lxq1qxJZmYmNWvWtGpHKUVaWho7duwotG/zKXgs7rnnHtLS0grle/XVVwkODrZKS0pKwtPT0yjv4eHB77//TqNGjYw8t956K7GxsSQmJlK7dm2+/vprOnbsSGpqKnPmzGHw4MG89NJL5OXlERMTY2VLeno6a9asYe7cuWVyhEWRkZHhsN+lMx2Mrdtlm9PvIvIIEAjcXdKySqmFwEIAHx8fFRQUxC05T5GRc9auO53yJDY2tkJtyMtTTJ++jv/97zBvvRXCiBH+/PTTT07dDxW9D0piw6FDh4z3ET74oLxa194rsvXug5ubG25uboXSRYS6devy2GOP0bdvX+bNm8fixYsJDQ3Fw8OD2NhY1q5dy4IFCwDIysriwoULuLu7ExISgre3NpWZnZ3NzJkz2bhxIy4uLpw+fZr09HT+9jdt/s3Dw4O6devi4uJSyIbU1FSaNm1qpNepU4ctW7bQs2dPXFxcmDZtGnfccQexsbH06NGDb775xig7atQoateubVXnuHHjCAoKokePHka6t7c3a9asKfKdkKysLGJiYnj//ffx8PCgS5cubNmyhfvuuw93d/dC+09E8PDwwN3dnVq1atmsu+Cx+OWXX67ZfkHyj01++fx9Z1lfYGAg06ZNY8iQIdStW5dOnToZ+2Pp0qXMmzePRx55hC+++ILnnnuOH3+8GlD4u+++46677jKOYXng7u5O3bp1HfK7dKaDSQZusVj3Ak4VzCQifYAZwN1KqUyLskEFysba23CjvEzy8q6U0NyqzenTqTRr5oG3d33i48fTtGldZ5tkUgx+fn589dVXVmmXL18mKSmJli1bUqdOHRo1akR8fDyRkZF8/PHHgHaX/tVXX+Hj42NVdtu2bdxwww3G+vLlyzl37hw7d+6kZs2aeHt7261aULt27UJ5CzoSe/nXv/7FuXPn+Pjjj7ly5ervMiMjg9q1axdZdu3atVy6dAl/f39Au8OvU6cO9913H40aNeL06dNW+VNTU2nQoAF+fn7GgwbF0aNHD5u9hTfffJM+ffpYpXl5eZGUlISXlxc5OTlcunTJ5lzJmDFjGDNmDADTp0833p4PCwszhh0feOABxo4da1UuIiKiygyPgf1qyuVBHNBaRG4VkVrAQ0C0ZQYR6Qh8DAxUSp212PQ9ECIinvrkfoieZhczGgUR6dqkzF+gKnD5ciYTJnxLz55LyMrK5amnOpvOpYrQu3dv0tPTWbp0KaAN3UyePJlRo0YZ4/cPPfQQr7/+utVF9p577uG///0v+WGbdu/ebbP+S5cuceONN1KzZk02bNjAiRMn7LatTZs213xCqiQsWrSI77//nvDwcFxcrC9HR44cMZ5gi4qKYtq0aYXKh4eHs2jRIhITE0lMTOS3337jhx9+ID09nZ49exIdHW04h5UrV9K+fXtq1KhBr169yMzM5JNPPjHqiouL46efCg8Vb9q0yZhgt/wUdC4AAwcONB5U+PLLL+nVq5fNB2fOntUuZydOnGDlypWG07jpppv4+eefAW0OrnXr1kaZS5cu8dNPPzFo0KAi9mjlwmkORldpnojmGA4BXyilDojIbBEZqGd7A01JYIWI7BGRaL3sn8C/0ZxUHDC7RE+8KYW6Pl/xsWLHjlP4+39IZmYucXHjqFXr+tcPu54QEaKiolixYgWtW7emTZs2uLu785///MfIM2zYMCIiIqwmwmfOnEl2djYBAQG0a9eOmTNn2qx/xIgR7Nixg8DAQJYvX06bNm3stu2GG26gZcuWHC3jG8vjx4/nzJkzdO3alQ4dOjBv3jxj24YNG7jvvvsAOHbsGPXq1bMqm56ezvfff2/kybfrrrvu4uuvvyYgIICJEydy11130aFDBz766CMWLdICDebv25iYGFq2bImfnx+vvPKKzUn1kjBmzBhSUlJo1aoVb7/9tvF9Tp06ZfVAw9ChQ2nbti0DBgxgwYIFeHpqD8F+8sknzJgxg/bt2zN9+nQWLlxolImKiiIkJMSqF1rpUUpVm0+bNm2UUkqNqtdBPeXioZzNhg0byqXe8+evqKSkS+rs2TT144/HnGKDvTi7/aJsOHjwYIXZcPny5Qpry1E2rFy5Us2YMaNcbPjjjz9Ur169jPQRI0aos2fPOrSt4mxwFs5u/+DBg4V+E8AOVYprbrUU5DqQeZatxnTO9YNSii++OEC7dh+yZk0CTZrcQO/eLZxtlsl1yv3330/z5s3Lpe4TJ07w1ltvGeufffYZTZpUj2Ht6wlnTvI7letRLWb06Gi2bz9JVFQoXbo4TnLbxORaFJyEdhSdO3cul3pNKpZq6mAU14vMmlKK775LoH//1jzzzB34+TXBza2aHlYTE5NKRbW9El0P7uX48QuMG/c1ly9n0q3bLXTq1MzZJpmYmJgYVMs5mLquDWjg4tygRmVl//6z3HHHJ/Tv34otW8bg6Vn0+wImJiYmFU217MG49zyILqha5Thw4CwnT6bSt28Ldu16kr//vXprqpmYmFReqmUPpirGg8nKymX27J8ICgrj7NkriIjpXKoBIsKjjz5qrOfk5NCkSZNCEvgFsSWTX9n466+/uPvuu8nNzTV0ubp3707btm0ZP348eXl5NsMFWMrwT506ldtuu42AgADuv/9+Ll68CMC+ffsYNWqU3bbMnz8fd3d3Ll26ZKQtWbKEiRMnWuULCgpix44dgKZh9+STTxrv0fTs2ZNt27aVZlcYKKWYOnUqrVq1IiAggF27dtnMFxkZSUBAAH5+fobicr7NTZo0MaT989/7AU0sNT994MCBtqp1ONXSwbhs60HTfQUjA1RuJkz4lu3bT7J795M88khA8QVMrgtuuOEG9u/fz19//QVATEwMN99sU9e1yrF48WKGDBlCjRraC8AtW7Zk8+bNxMfHc/DgQVatWlVsHX379mX//v3Ex8fTpk0b5s6dC4C/vz/Jycl2qxOEh4fTuXNnoqKi7LZ/7NixNGzYkISEBA4cOMCSJUuMWDOlZc2aNRw7doyEhAQWLlzIU089VShPSkoKU6dOZd26dRw4cIAzZ86wbt06Y3toaKihNmD5lF/t2rWN9Ojo6EL1lgfV0sGcyDjFsRzHxZ0uL9LTs5k1awN//vkXb711D19/PRwvr3rFFzQpH4KCiv9YBrgKCoIlS7Tl8+cL57WTe++9l2+//RYoLNV+5coVRo8eTefOnenYsSOrV68uVH779u1069aNjh070q1bNw4fPgxod7tDhgyhX79+tG7d2uqN//DwcPz9/WnXrp1VTJO6devywgsvcPvtt9OnTx+2b99OUFAQLVq0MC5aiYmJ9OjRg06dOtGpU6drikUuX77cpuyJq6sr3bp1s0slICQkBFdXbaS/S5cuJCcnG9sGDBhAREREsXUcO3aMtLQ0Xn31VcLD7Yr4wbFjx9i2bRuvvvqqIXHTokULK1WB0rB69WqGDx+OiNClSxcuXrxYSE/t+PHjtGnTxngvqE+fPoU06yoL1dLBnK7TkiT31sVndCKxsYm0b/8Rx45dQClFvXpuZjCwaspDDz1EREQEGRkZxMfHc+edV6NazJkzh169ehEXF8eGDRuYOnWqlWAkwG233cbGjRvZvXs3s2fPZvr06ca2PXv2EBkZyb59+1i5ciVJSUmcOnWKF154gfXr17Nnzx7i4uKM3sSVK1cICgpi586deHh48NJLLxETE0NUVBSzZs0C4MYbbyQmJoZdu3YRGRnJs88+W+g7ZWVlcfz4cZsvaqanp7Nu3TpDW+3YsWPG0E6+5IstFi9ebCXtHxgYyKZNm4rdv/lOu0ePHhw+fJhz54q/+Txw4AAdOnQwel9FERoaamV//idfY86SkydPGsKXoIlnnjx50ipPq1at+PXXX0lMTCQnJ4dVq1aRlHQ1eslXX31FQEAAw4YNs0rPyMggMDCQLl262NU7dATVcpJfVfJJmDNn0hg7Npr58+9hwACf4guYVAwljY9hmb9x45KX1wkICCAxMZHw8HArPSuAH374gejoaGNOIiMjo9Cw0KVLlxg5ciQJCQmIiBGWGDRBzfr1tbk8Hx8ffv/9d1JSUggKCjLukEeMGMHGjRsZPHgwtWrVol+/foA2DOXm5kbNmjXx9/c3xC+zs7OZOHEie/bsoUaNGjbDEJ8/f54GDRpYpR07dozu3btTo0YNBg0axL333ktiYiItW7Zkj0W0t/yAXpbMmTMHV1dXRowYYaTdeOONRgCzooiIiCAqKgoXFxeGDBlCVFQUkydPvuYNXUlv9CIjI+3Oa+vaVLA9T09PPvzwQ0JDQ3FxcaFbt24cP34c0Hptw4cPx83NjY8++oiRI0eyfv16QFNHuOmmmzh+/Di9evXC39+fli1blui7lJRq6WC80g+Tl104gJCz+eabI2zZksScOb359deJuLpWyw6miQ0GDhzIlClTiI2NJSUlxUhX15Dmtwz9O3PmTIKDg4mKiiIxMdEqzoebm5uxXKNGDXJycoq8AatZs6ZxwXNxcTHKu7i4GGGY58+fT9OmTdm7dy95eXm4u7sXqseW3H/+HExR8V9sERYWxjfffMO6deusLsb2yP3Hx8eTkJBA3759Aa1n5e3tzeTJk2nUqBEXLlywyv/nn3/SuHFjGjRoYHy/girQBQkNDTWGJS2ZNGkSjz32mFWal5eX1TBfcnKyTQHOAQMGMGDAAAAWLlxo9KQsA5uNGzfOangzv54WLVoQFBTE7t27y93BOPUKJiL9ROSwiBwVkRdtbO8pIrtEJEdEhhXYNlJEEvTPyJK0600NWpFXVvMdxrlzV3j44a94/vm19OmjaYeZzsXEktGjRzNr1ixj2Cgfe6T5L126ZDwYsCR/TqgI7rzzTn766SfOnz9Pbm4u4eHh3H333cWWs2yvWbNmuLi4sGzZMnJzcwvl8fT0JDc31+74M9di7dq1vPbaa0RHRxshDPKxlPvfvn17oYs5aMNjr7zyiiH3f+rUKU6fPs3vv/9O586d2bx5M3/88QcAO3bsIDMzk1tuuYWWLVsSGBjIyy+/bOz7hIQEm3NgkZGRNuX+bdkzcOBAwsPDUUqxdetW6tevT7NmhV+gzpf7v3DhAh988IExmW85XxMdHY2vr6+RLzNT0188f/48mzdvpm3b8n/QyWk9GBGpASwA+qIFEIsTkWil1EGLbCeAUcCUAmUbAi+jRblUwE69rPXtxjX4v3odaZaaUPYv4SA+/XQ3N9/sQXz8U9SpU9PZ5phUQry8vHjuuecKpc+cOZPnn3+egIAAlFI0b968UNCv//u//2PkyJG8/fbb9OrVq9i2mjVrxty5cwkODkYpRf/+/UsUg+Tpp59m6NChrFixguDg4GvKy4eEhPDzzz/bjKtiLxMnTiQzM9PogXTp0sWYo7GU+z9x4oTN3kxERARr1qyxSvvHP/5BREQEL7zwAu+++y79+/cnLy+PunXrWsWtWbRoEZMnT6ZVq1ZG8Lc33nij1N8FoH///qxatcqo83//+5+xrUOHDsZQ4XPPPcfevXsBmDVrlhFq4b333iM6OhpXV1caNmxo3FAcOnSIJ598EhcXF/Ly8njxxRcrxME4TTof6Ap8b7E+DZh2jbxLgGEW68OBjy3WPwaGF9dmvlz/piaDVUIdf/u0q8uJpKRLqlu3/6pffjnhVDucLZfv7PaLssGU6y9fdu3apR555JFysSEjI0PdeeedKjs7Wyml1JQpU9TevXvtKuvsY+Hs9h0p1+/MOZibgSSL9WTgzmvktaeszZcDROQJ4AmAJk2aEBsby7uXdlErJ5WnSjnpWhby8hTffHOaxYsTue++xqSlJRAbe6zC7cgnLS2NWCfsh8rSflE21K9f32ao3PIgNze3wtqqLDa0atWKrl27cvHiRWMOwVE2HD16lFmzZhnvD+U/4WZP3c4+Fs5uPyMjw2G/S2c6GFuPYtj7eJfdZZVSC4GFAD4+PiooKIgpKh3IsprsrAhyc/PIzs7j00+j2bx5LOfOHaxwGwoSGxvrVBuc3X5RNhw6dKjEE86lJTU1tcLaqkw2PP300+ViQ8eOHenYsWOpyjr7WDi7fXd3d+rWreuQ36UzZ5KTgVss1r2A4p8pLHtZVAVHg8nJyePNN38hODgMN7caLF8+BD+/GyvUBhMTE5OKxpkOJg5oLSK3ikgt4CHAXv2C74EQEfEUEU8gRE+zD1Vxcv3795+lW7dPWbPmKEuWDDZfljQxMak2OG2ITCmVIyIT0RxDDWCxUuqAiMxGm1CKFpHOQBTgCQwQkX8ppfyUUn+KyL/RnBTAbKXUn/a2LRZ/y4vMzBxEhPPn03nyydsZPbqj6VxMTEyqFU590VIp9R3wXYG0WRbLcWjDX7bKLgYWl6bdxu7e1MhNL01Ru9i2LZkxY6KZPr0HDz/sT1BQ83Jry8TExKSyUi3f5svqvJPUwEMOrzcvTzFp0vcMHhzJrFl3M3x4u+ILmZgUgYgwefJkY/3NN9+0KZVSGlatWsXs2bMBTX7Fx8eHDh060K5dO0O40lIaP5/mzZtz/vx5kpKSCA4OxtfXFz8/P959910jz5QpUwyJEnto3769lYgnWEvjA4Wk+7dv307Pnj3x8fHhtttuY+zYsaSnl+3G8bfffiM4OJjWrVsTGhpKVlZWoTxZWVk8/vjj+Pv70759e6unra4low/wxRdf0LZtW/z8/Hj44YfLZGdVoVo6mPKQIktOvoyLi+Dr25h9+57iwQf9zCExkzLj5ubGypUryywDb4vXX3/d6imuCRMmsGfPHlasWMHo0aPJyyta7cLV1ZW33nqLQ4cOsXXrVhYsWMDBg9p70s888wzz5s2zy45Dhw6Rl5fHxo0bCwl1XoszZ87wwAMP8Nprr3H48GEOHTpEv379yvx47wsvvMCECRNISEjA09OTTz/9tFCeTz75BNBizsTExDB58mTy8vKKlNFPSEhg7ty5bN68mQMHDvDOO++Uyc6qQrV0MC7b2uC+/VaH1HXxYgbjxkXTu/dSsrJyGTfudho3rlN8QZMqR1BQUKHPBx98AGgKwLa2579Jff78+ULb7MHV1ZUnnniC+fPnF9p27tw5hg4dSufOnQ1Zk7y8PJo3b24E3gLtfRNLbTLQZFTc3Nxo3LhxoXp9fX1xdXUt1qk1a9aMTp06AeDh4YGvr6+h/Ovt7U1KSoohs1IUn3/+OY8++ighISF89913xeYHWLBgASNHjqRr166A1tMbNmwYTZs2tau8LZRSrF+/nsGDBwMwcuRIm6rDBw8epHfv3oAmqNmgQQN27NhRpIz+J598woQJE/D09DTKVQeqpYNJzssmyQFaZNu3n6Rduw+oWbMGcXHjqFWreOluE5OSMmHCBJYvX24VbRE0uZB//vOfxMXF8dVXXzF27FhcXFwYNGiQEThr27ZtNG/evNCFd/PmzYZzKMi2bdtwcXExLpTz58+3kpm3pVCcmJjI7t27rUIJdOrUic2bNxf7/SIjIwkNDWX48OF8+eWXxeYH2L9/P7fffnux+Q4fPmxTKr9Dhw5WThi0QF4NGjQw4svYksoHbThv9erV5OTk8Ntvv7Fz506SkpKKlNE/cuQIR44coXv37nTp0oW1a9fa9T2rOtVSTfmM+98py1NkZ89eISMjhxYtPPn886H07OntOONMKi1Fvdlcp06dIrc3bty41G9G16tXj8cee4z33nvPSk/rxx9/NIakAC5fvkxqaiqhoaHMnj2bxx9/nIiICEJDQwvVefr0acOB5LNgwQJWrFiBh4cHkZGRxhDvP//5T6ZMuSoHWDCGS1paGkOHDuWdd96hXr2rAfHskcuPi4ujSZMmeHt74+XlxeOPP86FCxfw9PS0OcRc0mFnHx8fK6n/olB2SOWDJjx66NAhAgMD8fb2plu3bri6uhYpo5+Tk0NCQgKxsbEkJyfTo0cP9u/fXyhkwfVGtezBaJTcwSil+OyzePz9P+THH4/TuHEd07mYVAjPP/88n376qdUcRV5eHlu2bDHUeU+ePImHhwddu3bl6NGjnDt3jlWrVjFkyJBC9dmSy8+fg9m0aRM9evSwy67s7GyGDh3KiBEjCrVjj1x+eHg4v/76K82bN6dly5akpqYaw0oF5fLzpfIB/Pz82LlzZ7H2laQH07hxYy5evGiEHbiWVL6rqyvz589nz549rF69mosXL9K6tRbAcMCAAWzbto0tW7bg4+NjpHt5eTFo0CBq1qzJrbfeio+PDwkJlUdwt7yolg6m+ZUDtLyyt8TlHntsFW+++Qvfffcwo0eXTobCxKQ0NGzYkAcffNBq0jkkJIT333/fWM+/UxcR7r//fiZNmoSvr69VjJB8fH197QpJXBRKKcaMGYOvry+TJk0qtN1SLn/atGmF4t3n5eWxYsUK4uPjDbn88PBwI2xxUFAQn332mdGzCAsLIzg4GNBUlMPCwti2bZtR32effVZozie/B2PrU7D3ICIEBwcb8y5hYWE2VaTT09MNRx8TE4Orq6uhTHwtGf3BgwezYcMGQJuPO3LkCC1atCh6B18HVEsHE+ByAwEu9k3E5+UpVq36FaUUU6d2Iy5uHLffXviuxsSkvJk8ebLVxPt7773Hjh07CAgIoG3btlahhENDQ/nss89sDo8B9OzZk927d5cpuuvmzZtZtmwZ69evN3oF+ZP02dnZHD16lMDAQEB74upvf/ubVfmNGzdy8803G7FqALp3787Bgwc5ffo0TzzxBB4eHrRv35727duTlpZmDNU1bdqUiIgIpkyZgo+PD76+vmzatMlqiK40vPbaa7z//vu0atWKlJQUxowZA2ixVfIFM8+ePUunTp3w9fXltddeY9myZUb55557jrZt29K9e3defPFFQ0b/nnvuoVGjRrRt25bg4GDeeOMNm47/uqM0EsxV9ZMv17+zfpDaW7/HNeWq8zl8+Lzq0WOx6tp1kfrzz/Ri85eUyixVX13aL8qG612u/9lnn1UxMTHlYsPKlSvVSy+9ZKyHhITYVc7ZUvWVwQZnt+9Iuf5q2YMRpVDFzMHka4gNG9aWTZsex9Oz6LFkE5OqxvTp08v8YuK1yMnJsXpB9Pvv7ZcKNLl+qJZPkU1NP4CrCLYeFNy79w9OnUqlX79W7N07nptvLluX28SkstK0aVMGDhxYLnU/8MAD5VKvSdWiWvZgUlQWF1S2VVpmZg4zZ66nb99lXLyYgYiYzsWkTHMUJiZVDUef79WyB6NhPUT29NPfcuFCBnv3jqdZM+cGfjKpHLi7u5OSkkKjRo1M2R+T6x6lFCkpKbi7uzuszmrpYJTSAsKkpWUxZ85Gpkzpxrvv3kvdurWcbZpJJcLLy4vk5GTOnTtX7m1lZGQ49Idt2lB1bXBm++7u7nh5efH77787pL5q6WAEuJz3d/z9P6RnT29cXMR0LiaFyH8priKIjY0tdYhf04brywZnt+9InDIHIyINRSRGRBL0/57XyDdSz5MgIiMt0ueISJKIpJWm/ca1A0nmHj788D7CwgabT4iZmJiYlAPOmuR/EVinlGoNrNPXrRCRhsDLwJ3AHcDLFo7oaz2tVIwPW0/Yirn069eqtFWYmJiYmBSDs4bIBgFB+nIYEAu8UCDPPUCM0kMhi0gM0A8IV0pt1dNK1fjQoVBNH6AzMTExqTCc5WCaKqVOAyilTouIreAINwNJFuvJelqJEJEngCf01UwR2V/SOsqRxoDjI0lVLRuc3b5pg2lDZbPB2e3bsqFUqr7l5mBE5EfgbzY2zbC3ChtpJX5IWym1EFio27RDKRVY0jrKi8pgj7NtcHb7pg2mDZXNBme370gbys3BKKX6XGubiJwRkWZ676UZcNZGtmSuDqMBeKENpZmYmJiYVAGcNRERDeQ/FTYSWG0jz/dAiIh46pP7IXqaiYmJiUkVwFkOZh7QV0QSgL76OiISKCKLAPTJ/X8DcfpntsWE/+sikgzUEZFkEXnFznYXOvZrlJnKYI+zbXB2+2DakI9pg4azbXB2++AgG8TUWjIxMTExKQ/MZ3VNTExMTMoF08GYmJiYmJQL152DcbYMjV6/oy5qAAAKJ0lEQVRHPxE5LCJHRcSWSkFPEdklIjkiMsweu8rBhkkiclBE4kVknYh4W2yrKBvGi8g+EdkjIj+LSFuLbdP0codF5J7yssEi3zARUSISaJFWITaIyCgROafvhz0iMtZiW5mPhT37QEQe1M+HAyLyuSPbt8cGEZlv8f2PiMhFJ9jwdxHZICK79d9Ff4ttFXUueOu/x3gRiRURL4ttjjgXFovIWbnG+4Ci8Z5uX7yIdCpT+6UJg1mZP8DrwIv68ovAazbyNASO6/899WVPfVsXoBmQVsr2awDHgBZALWAv0LZAnuZAALAUGGaPXeVgQzBQR19+Coh0gg31LJYHAmv15bZ6fjfgVr2eGuVhg57PA9gIbAUCK9oGYBTwfknOUwe33xrYbfEbuLGiz4UC+Z8BFjvhfFwIPGVx/BOdcC6sAEbqy72AZQ7eDz2BTsD+a2zvD6xBew+xC7CtLO1fdz0YNBmaMH05DBhsI48hQ6OUugDky9CglNqqdJWBUnIHcFQpdVwplQVE6DYZKKUSlVLxQJ69dpWDDRuUUvnxcreivWdU0TZctli9gasv0g4CIpRSmUqp34CjlE57rlgbdP6NdmOSYZFW0TbYwhHHwp72xwEL9DZQSuW/l1Zh50IBhgPhTrBBAflRBusDp/TlijwX2qLpMwJssNjukP2glNoI/FlElkHAUqWxFWgg2ruKpWr/enQwVjI0QLnJ0FyDstTtKLtKWs8YtLuWCrdBRCaIyDG0C/yzFW2DiHQEblFKfVMa+x1hg85QfUjiSxG5xYE22FNHG6CNiGwWka0i0q8EZR1lA6ANEaH1EtY7wYZXgEdEewXiO7SeVEXbsBcYqi/fD3iISCMH2lBaG0vVfpV0MCLyo4jst/Gx987QITI05VC3o+yyux4ReQQIBN5whg1KqQVKqZZoYqcvVaQNIuICzAcml7Sso2zQ+RporpQKAH7kag/cETbYU4cr2jBZEFrvYZGINHBQ+/bakM9DwJdKqdxSlC2rDcOBJUopL7ShomX6OVKRNkwB7haR3cDdwEkgx4E2FMe12ilV+1XSwSil+iil2tn4rAbO6F06pGgZmlss1r242h0uK2Wp21F22VWPiPRB04YbqJTKdIYNFkRwdTizomzwANoBsSKSiDbmHK1P9FfYflBKpVjs/0+A2+0t64j29TyrlVLZ+hDQYTSH44xz4SGuDo+VtGxZbRgDfAGglNoCuKOJPlbkuXBKKTVEKdURXbdRKXXJgTaU1sbStV/SSaLK/kG7E7ec5H/dRp6GwG9ok1We+nLDAnlKO8nvijYBditXJ/L8rpF3CYUn+Yu0y1E2AB3RJhxbl3TfONCG1hbLA4Ad+rIf1pOqxyndpKrdx0LPH8vVSf4KswFoZrF8P7DVUcfCzvb7AWH6cmO0oZBGFXku6Pl8gET0F8CdcD6uAUbpy75oF1Cp4HOhMeCiL89BUzBx2H7Q62rOtSf578N6kn97WdovsXGV/aP/MNYBCfr/hnp6ILDIIt9otMm6o8DjFumvo3nrPP3/K6WwoT9wBO0CPkNPm43WUwDorNd9BUgBDhRnVznY8CNwBtijf6KdYMO7wAG9/Q2WPza0u7djaHfT95aXDQXyxqI7mIq0AZir74e9+n64zZHHwo72BXgbOAjsAx6q6HNBX38FmGejbEWdj22Bzfpx2AOEOOFcGIZ27ToCLALcHHwuhAOngWy0a9AYYDww3uJcWKDbt6/A76HE7ZtSMSYmJiYm5UKVnIMxMTExMan8mA7GxMTExKRcMB2MiYmJiUm5YDoYExMTE5NywXQwJiYmJiblgulgTCodIpJroay7R0SaF5G3+bWUYUvYZqyucrtXl0zxKUUd40XkMX15lIjcZLFtkVioRTvIzjgR6WBHmedFpE4p2npHRHoWaDf/mAzT0/OP1X4RWZHfToH0r3VlAESkiYisLaktJlUT08GYVEb+Ukp1sPgkVlC7I5RS7dGkWt4oLnNBlFIfKaWW6qujgJssto1VSh10iJVX7fwA++x8HiiRgxGRhkAXpYkjWrabf0y+1NPyj1U7IAvtnYqC6X8CEwCUUueA0yLSvST2mFRNTAdjUiXQeyqbRIujs0tEutnI4yci2/U753gRaa2nP2KR/rGI1CimuY1AK71sbz0+yD49loabnj5PrsbTeVNPe0VEpuh394HAcr3N2noPIFBEnhKR1y1sHiUi/y2lnVuwEBwUkQ9FZIdoMV3+pac9i+boNojIBj0tRES26PtxhYjUtVH3MKCkPY1N+futKDuBVcCIEtZtUgUxHYxJZaS2xVBMlJ52FuirlOoEhALv2Sg3HnhXKdUB7QKfLCK+ev7uenouxV/cBgD7RMQdTc4nVCnljyb18ZR+d38/mvJAAPCqZWH97n4HV+/4/7LY/CUwxGI9FIgspZ390C7W+cxQSgWixRq6W0QClFLvoUmeBCulgkWkMZqoaB99X+4AJtmouzuws0Dacovj0shyg4i4Aveivf1tmV4D6A1EWyTvAHoU891MrgNcnW2AiYkN/tIvspbUBN7X5xxy0STmC7IFmCFaFMCVSqkEEemNJh4ZJyIAtbEtgAraBfQvND2sZ9C0sX5TSh3Rt4ehDfW8jxY7ZpGIfAsUlPq/JkqpcyJyXES6oEmC+KDJk0wooZ03oAWw6mSR/qCIPIH2u26GJn0SX6BsFz19s95OLbT9VpBmwLkCaSOUUjsKpNUWkT368ibg0wLpzdEcVYxFmbNYDB+aXL+YDsakqvBPNO209mg974yCGZRSn4vINjTBvu9FCz0saEKO0+xow+oCWvAu3aKdHBG5A+3O/CFgIlr0QXuJBB4EfgWilFJKtKu93Xai6WXNQ9ONGiIit6JJvXdWSl0QkSVoasAFEbTAUcOLaeOva5QvlM/GzYCRLiL10RzwBK72Ot31+k2uc8whMpOqQn3gtFIqD3gU7e7dChFpARzXh4Wi0YaK1gHDRORGPU9D0YJa2cOvQHMRyZ9XeBT4SZ+zqK+U+g5tAt3WBTYVLRyALVaihSYYjuZsKKmdSqlstKGuLvrwWj008dRLItIUbbjKli1bge7530lE6oiIrd7gIWzPp5QIpUnNPwtMEZGaenIboMxP/plUfkwHY1JV+AAYKSJb0S5QV2zkCQX260Mzt6GFfj2IdiH+QUTi0YZqmtnToFIqA3gcWCEi+9AUtj9Cu1h/o9f3E1rvqiBLgI/yJ/kL1HsBTbnYWym1XU8rsZ363M5bwBSl1F5gN5oq82K0Ybd8FgJrRGSD/hTXKCBcb2cr2r4qyLdoAcjKjFJqN1qP6yE9KViv3+Q6x1RTNjExsYmI/Az8Qyl10cH1bgQG6Y7W5DrGdDAmJiY2EZE70eZSCj4oUJY6m6A9Kbeq2MwmVR7TwZiYmJiYlAvmHIyJiYmJSblgOhgTExMTk3LBdDAmJiYmJuWC6WBMTExMTMoF08GYmJiYmJQL/w82dlNERvnoYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lw = 1\n",
    "plt.grid(True)\n",
    "plt.plot(fpr_ph, tpr_ph,'-b',label='Overall (PH2), AUC = '+str(round(auc_ph,3)))\n",
    "plt.plot(fpr_ph_mel, tpr_ph_mel,'-.r',label='Melanoma (PH2), AUC = '+str(round(auc_ph_mel,3)))\n",
    "plt.plot(fpr_ph_ben, tpr_ph_ben,'--k',label='Nev (PH2), AUC = '+str(round(auc_ph_ben,3)))\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([-0.01, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.xticks(axisRange)\n",
    "plt.yticks(axisRange)\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class wise ROC curves for the segmentated masks using the proposed DSNet for mel, sk, and nev classes of ISIC-2017 test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lw = 1\n",
    "plt.grid(True)\n",
    "plt.plot(fpr_isic, tpr_isic,'-b',label='Overall (ISIC-2017), AUC = '+str(round(auc_isic,3)))\n",
    "plt.plot(fpr_isic_mel, tpr_isic_mel,'-.r',label='Melanoma (ISIC-2017), AUC = '+str(round(auc_isic_mel,3)))\n",
    "plt.plot(fpr_isic_sk, tpr_isic_sk,'k',label='Sk (ISIC-2017), AUC = '+str(round(auc_isic_sk,3)))\n",
    "plt.plot(fpr_isic_ben, tpr_isic_ben,'--k',label='Nev (ISIC-2017), AUC = '+str(round(auc_isic_ben,3)))\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([-0.01, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.xticks(axisRange)\n",
    "plt.yticks(axisRange)\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC curves for the segmentated masks using the proposed DSNet, UNet, and FCN8s on ISIC-2017 test dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lw = 1\n",
    "plt.grid(True)\n",
    "plt.plot(fpr_isic, tpr_isic,'-b',label='Proposed DSNet with AUC = '+str(round(auc_isic,3)))\n",
    "plt.plot(fpr_unet, tpr_unet,'-.k',label='U-Net with AUC = '+str(round(auc_unet,3)))\n",
    "plt.plot(fpr_fcn, tpr_fcn,'--r',label='FCN8s with AUC = '+str(round(auc_fcn,3)))\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([-0.01, 1.01])\n",
    "plt.ylim([0.0, 1.01])\n",
    "plt.xticks(axisRange)\n",
    "plt.yticks(axisRange)\n",
    "plt.xlabel('False Positive Rate (FPR)')\n",
    "plt.ylabel('True Positive Rate (TPR)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
